Access and Equity in Ontario Teacher Education: Teacher Candidates’ Perceptions
Bibliographic record
Abstract
Access, equity, and equitable representation are ongoing challenges in teacher education. While many Canadian teacher education programs identify equity and diversity as key values, these values do not always result in rates of representation that reflect the student population. Minoritized teacher candidates also experience our programs in unique ways, creating gaps between university equity statements and the lived experiences of our students. This study therefore examines the perspectives of 13 teacher candidates who self-identify as members of various underrepresented groups. Participants’ experiences offer key insights into the challenge of achieving equitable and diverse representation. The presented findings will be of interest to teacher educators and other stakeholders committed to addressing the complex task of increasing equity and access for underrepresented groups in their programs. L’accès, l’équité et la représentation équitable constituent des défis constants en formation des enseignants. Alors que plusieurs programmes de formation des enseignants identifient comme valeurs fondamentales l’équité et la diversité, l’adoption de ces valeurs ne mène pas toujours à des taux de représentation qui reflètent la population des étudiants. Les candidats minoritaires au programme de formation à l’enseignement vivent l’expérience du programme différemment, ce qui crée des écarts entre l’énoncé de l’université sur l’équité et le vécu des étudiants. Cette étude porte sur la perspective de 13 étudiants candidats au programme de formation à l’enseignement qui s’auto-identifient comme membres de divers groupes sous-représentés. Ces expériences offrent des aperçus essentiels sur le défi d’atteindre une représentation équitable et diverse. Les résultats sauront intéresser les formateurs d’enseignants et d’autres parties prenantes qui s’engagent à aborder la tâche complexe qui est celle d’augmenter l’équité et l’accès à leurs programmes pour les étudiants sous-représentés. Mots clés : accès; équité; formation des enseignants; groupes sous-représentés; admissions
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".